Aerospace, automotive and other industries have been utilizing several welding processes known in the art in order to join various parts, segments, assemblies and fuselage segments. The welding processes enable joining of two metals or materials by forming a joint between them. However, the welding processes may not always result in creating a strong weld at the interface of the two materials resulting in a defective weld. The defective weld may be due to formation of void or cavity at the junction of the weld joint. Considering the critical applications such as aerospace and defense, it is of utmost importance to ensure that the weld joints are of high weld strength, and there are no defects in the weld joints due to formation of void, cavity or distortions at the interface of the materials being welded. Therefore, the materials welded using the welding processes needs to be checked to verify whether or not the weld formed is defective.
Conventionally, an X-Ray image of the weld joint is obtained and provided to a person, who manually inspects the X-Ray image to locate and identify the defects in the weld joint. Specifically, the person has to visually scan the X-Ray image to identify any defect in form of void, cavity, crack or distortions with the help of naked eye. However, this method of manual inspection is onerous and cumbersome. Further, identifying defective welds in a plurality of X-Ray images is challenging and time consuming process. Also, there is always possibility of human errors while identifying the defects in the X-Ray images. Hence, there was a need to automate the process of identifying the defects in the weld joints, which would potentially lead to a reduction in expenses and errors.